Ph.D. Dissertation Defense Warming-enhanced Plant Growth in the North Since 1980s: A Greener...
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Ph.D. Dissertation Defense Warming-enhanced Plant Growth in the North Since 1980s: A Greener Greenhouse? Liming Zhou Department of Geography, Boston University
Ph.D. Dissertation Defense Warming-enhanced Plant Growth in the
North Since 1980s: A Greener Greenhouse? Liming Zhou Department of
Geography, Boston University Dissertation Committee Ranga B. Myneni
Robert K. Kaufmann Yuri Knyazikhin Nathan Phillips Compton J.
Tucker 1 of 43
Slide 2
Summary of Presentation 1.Motivation 2.Data 3.Quality of
Satellite Data 4.Changes in Northern Vegetation Activity 5.Spatial
Pattern of Changes in Vegetation 6.Drivers for Changes
7.Contributions of Research 8.Future Directions 2 of 43
Slide 3
Has Vegetation Responded to Climate Change? Pronounced warming
in northern high latitudes Earlier disappearance of snow in spring
Increased precipitation in northern high latitudes Increased
concentration of atmospheric CO 2 Changes in Climate Increased
productivity through: - enhanced photosynthesis - enhanced nutrient
availability Changes in Vegetation 3 of 43
Slide 4
i. greatest warming in winter and spring ii. continental
difference: overall warming in Eurasia and smaller warming or
cooling trends in North America coolingwarming Monthly land surface
climate data (1981-1999) 1. NOAA precipitation: 2.5 x2.5 2. GISS
temperature: 2 x2 Data 4 of 43
Slide 5
Satellite NDVI data at 8 km resolution 1. GIMMS 15-day
composite NDVI (07/81-12/99) 2. Pathfinder AVHRR Land 10-day
composite NDVI (07/81- 09/94) Solar zenith angle (SZA) from GIMMS
and Pathfinder data Monthly stratospheric aerosol optical depth
(AOD) reported as zonal means A land cover map at 8 km resolution 5
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Slide 6
Factors that may contaminate long-term satellite measures: 1.
calibration uncertainties (satellite drift and changeover) 2.
atmospheric and bidirectional effects (aerosol, vapor, etc) 3. soil
background effects Methods that help to reduce some non-vegetation
effects 1. Maximum NDVI compositing 2. Spatial and temporal
aggregations 3. Empirical methods Changes in SZAChanges in AOD El
ChichonMt. Pinatubo 6 of 43
Slide 7
Are AVHRR Satellite Measures of NDVI Contaminated by Satellite
Drift and Changeover? Kaufmann, R. K., Zhou, L., Knyazikhin, Y.,
Shabanov, N.V, Myneni, R.B., and Tucker, C.J., Effect of orbital
drift and sensor changes on the time series of AVHRR vegetation
index data. IEEE Trans. Geosci. Remote Sens. 38: 2584-2597, 2000. 7
of 43
Slide 8
Theoretical Analysis The effect of changes in SZA on NDVI can
be examined from radiative transfer equation in vegetation media.
NDVI = f (SZA, ) Sensitivity experiments Result: NDVI is minimally
sensitive to SZA changes and this sensitivity decreases as leaf
area increases. 8 of 43
Slide 9
Empirical Analysis A statistically meaningful relation between
NDVI and SZA? 1. Ordinary least squares (OLS) 2. Cointegration
analysis (VECM) i. spurious regression results? i. a cointegrating
relation? ii. the statistical ordering of this relation? 9 of
43
Slide 10
Land cover type A statistically meaningful relation?
OLSCointegration analysis causal order Evergreen needleleaf forests
no Evergreen broadleaf forests yesno Deciduous needleleaf forests
no Deciduous broadleaf forests no Mixed forests no Woodlands no
Wooded grasslands/shrubs yes SZA NDVI Closed bushlands/shrublands
yes SZA NDVI Open shrublands yes SZA NDVI Grasses yes SZA NDVI
Relationship between NDVI and SZA 10 of 43
Slide 11
Conclusions Theoretical and empirical analyses indicate that
NDVI is minimally sensitive to SZA changes and this sensitivity
decreases as leaf area increases. Using OLS can generate spurious
regressions because of the nonstationary properties of time series.
The AVHRR NDVI do not cointegrate with satellite drift and
changeover for dense vegetation types. 11 of 43
Slide 12
Has Northern Hemisphere Vegetation Changed? Zhou, L., Tucker,
C.J., Kaufmann, R.K., Slayback, D., Shabanov, N.V, and Myneni,
R.B., Variations in northern vegetation activity inferred from
satellite data of vegetation index during 1981 to 1999. J. Geophys.
Res. 106, 20069-20083, 2001. 12 of 43
Slide 13
Study Pixels Vegetated pixels (defined by NDVI) between April
to October 1. Minimize the SZA effect 2. Reduce the soil background
contribution (snow, barren and sparsely vegetated areas) 3. Use
data from the same pixels in the entire analysis. Map of vegetated
pixels 13 of 43
Slide 14
Changes in Vegetation Activity Changes in vegetation
photosynthetic activity can be characterized by 1. changes in
growing season 2. changes in NDVI magnitude Increases in NDVI
magnitudeIncreases in growing season JanDecJul Aug Increase NDVI
JanDecJul Aug earlier spring delayed fall NDVI 14 of 43
Slide 15
Increases in Growing Season (Increased by 18 Days) (Increased
by 12 Days) 11.9 days/18 yrs (p
Define the persistence index (PI) of NDVI increase: PI = PI(1)
+ PI(2) + PI(3) + PI(4) + PI(5) +PI(6), 0 PI 6 PI (i) = 1 if Trend
i > 80% Trend i-1 0 otherwise Year PI (1) = 1 if Trend 1 >
80% Trend 0 0 otherwise 82 84 87 89 91 93 95 97 99 P(1), PI(2),
PI(3), P(4), PI(5), PI(6) Trend 0 (82-87) Trend 1 (82-89) Trend 2
(82-91) Trend 3 (82-93) Trend 4 (82-95) Trend 5 (82-97) Trend 6
(82-99) 20 of 43
Slide 21
Persistence index of NDVI 21 of 43
Slide 22
Consistency between NDVI and Temperature R=0.79 (p